Depth uncertainty is one of the major uncertainties associated with hydrocarbon field development. This uncertainty mostly arises due to the complexity of the subsurface, paucity of data, time-to-depth conversion, seismic picks, fault positioning and well ties. These uncertainties explain the non-uniqueness of models built and can have a significant impact on fluid contact and hydrocarbon in-place evaluation. To manage depth uncertainty, The Polynomial and Vo_K method were adopted to build velocity models for depth conversion and residual analysis for several reservoir levels to determine the method that will give the best depth residuals. Depth conversion residual analysis result of both velocity models for the reservoirs studied gave average depth residual of less than 50ft for reservoir levels below 9000ft. As the depth increases, the polynomial method derived average residual becomes unreliable with depth uncertainty of over 100ft for the deeper MOT reservoir, compared to 11. 65ft of the Vo_K method for the same reservoir. This was expected at depth since the polymonial method adopts average velocities while the Vo_K method uses instantaneous velocity. Hence, the latter is expected to give a better result at great depth during depth conversion and should be preferably employed for velocity modeling and depth conversion study of reservoir in the Niger delta Basin.

MOT reservoir has a unique case of uncertainties as a result of data paucity being in a field where no production has occurred, and there is need to reduce the uncertainties associated with the key evaluation parameters required for making investment decisions. This paper presents how a multidisciplinary team resource was leveraged on in managing the identified uncertainties to deliver a robust development plan for the reservoir of interest. The approach deployed emphasize on integration and collaborative interpretations from the constituting disciplines in the study team. Early focus was placed on uncertainty identification, quantification and management. Iterative efforts were necessary to achieve consistency of results and preservation of physical meaning as the study moves from one domain to another. A consistent framework for quantifying the respective impacts of the identified uncertainties was developed, and realizations were constrained by the most impacting parameters to generate a probable representation of the subsurface. Subsurface development concepts were tested and suitably selected to optimize recovery using the base case realization as a control, and preliminary economic evaluations were also performed to determine the project robustness to risk and the magnitude of the investment. The experience from this work provides a reliable approach to handling the development of a green field reservoir with limited data availability. An approach to overcoming several limitations on how to predict a fit-for-purpose PVT-table, developing a representative SHM were also presented, and the success obtained further emphasize the advantage of integration in a multidisciplinary team. The results showed that the high impacting uncertainties were structure, fluid contacts, and relative permeability, and the identified uncertainties were managed by building realizations to adequately capture the possible outcomes, and the preliminary project economic evaluations suggests that the project would be viable even for the Low-Case outcomes, hence adding value to the company portfolio.